1 out of 30 Digital Processing Algorithms for Bistatic Synthetic Aperture Radar Data 4 May 2007 by Y. L. Neo Supervisor : Prof. Ian Cumming Industrial

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Digital Processing Algorithms for BistaticSynthetic Aperture Radar Data

4 May 2007by Y. L. Neo

Supervisor : Prof. Ian CummingIndustrial Collaborator : Dr. Frank Wong

Sponsor : DSO National Labs (Singapore)

T H E U N I V E R S I T Y O F B R I T I S H C O L U M B I A

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Agenda

• Bistatic SAR principles• A Review of Processing Algorithms• Contributions

1. Point Target Spectrum

2. Relationship Between Spectra

3. Bistatic Range Doppler Algorithm

4. Non Linear Chirp Scaling Algorithm

• Conclusions

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Bistatic SAR

• In a Bistatic configuration, the Transmitter and Receiver are spatially separated and can move along different paths.

• Bistatic SAR is important as it provides many advantages

– Cost savings by sharing active components

– Improved observation geometries

– Passive surveillance and improved survivability

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ransmitter F

light Path

Receiver

Transmitter

Bistatic Angle

RT

RR

Target

Imaging geometry of monostatic/bistatic SAR

TargetMonostaticSAR Platform

RT

Platform

flight path

2 x

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Focusing problems for bistatic algorithms• Traditional monostatic SAR algorithms based on

frequency domain methods make use of 2 properties1. Azimuth-invariance2. Hyperbolic Range Equation

• Bistatic SAR data, unlike monostatic SAR data, is inherently azimuth-variant – targets having the same range of closest approach do

not necessarily collapse into the same trajectory in the azimuth frequency domain.

• Difficult to derive the spectrum of bistatic signal due to the double square roots term (DSR).

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Agenda






• Conclusions

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Overview of Existing Algorithms

• Time domain algorithms are accurate as it uses the exact replica of the point target response to do matched filtering

• Time based algorithms are very slow – BPA, TDC

• Traditional monostatic algorithms operate in the frequency domain – RDA, CSA and ωKA– Efficiency achieved in azimuth frequency domain by

using azimuth-invariance properties

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Existing Bistatic Algorithms

• Frequency based bistatic algorithms differ in the way the DSR is handled.

• Majority of the bistatic algorithms restrict configurations to fixed baseline.

• Three Major Categories1. Numerical Methods – ωKA, NuSAR – replace transfer

functions with numerical ones2. Point Target Spectrum – LBF3. Preprocessing Methods – DMO

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LBF (Loffeld’s Bistatic Formulation)

• Solution for the stationary point is a function of azimuth time given in terms of azimuth frequency (f )

• LBF derived an approximation solution - b(f) to

stationary phase

Using this relation, the analytical point target spectrum can beFormulated - LBF

ApproximateSolution toStationary phase

Quasi-monostaticTerm

BistaticDeformation Term

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Phase modulator Migration operator

Rocca’s smile operator transforms

Bistatic Trajectoryto

Monostatic TrajectoryMonostatic Trajectory

DMO (Dip and Move Out)• Transform Bistatic data to Monostatic (using Rocca’s

Smile Operator)• Assumes a Leader-Follower flight geometry

(azimuth-invariant)

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Agenda






• Conclusions

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Major Contributions of the Thesis#1

Derived an accurate point target spectrum using MSR

(Method of Series Reversion)

#3Derived Bistatic RDA -

Applicable to Parallel flight

cases with fixedbaseline

#2Compare Spectrum

Accuracy -MSR is more accurate than

Existing point targetSpectrum – LBF and

DMO

#4Derived NLCS

Algorithm – Applicableto Stationary

Receiver & Non-parallel Flight cases

Focused Real bistatic data. Collaborative

work with U. of Siegen (airborne-airborne data)

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Derivation of the analytical bistatic point target spectrum

• Problem : To derive an accurate analytical Point Target Spectrum

• POSP: Can be used to find relationship between azimuth frequency f and azimuth time

f = [1/(2)] d()/d

• But we have to find = g(f ).Difficulty: phase () is a double square root.

1 2 3 4 Contribution#

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Our Approach to solving the point target spectrum

• Approach to problem:– Azimuth frequency f can be expressed as a

polynomial function of azimuth time .– Using the reversion formula, can be expressed

as a polynomial function of azimuth frequency f

• Journal Paper Published : Y.L.Neo., F.H. Wong. and I.G. Cumming A two-dimensional spectrum for

bistatic SAR processing using Series Reversion, Geoscience and Remote Sensing Letters, Jan 17 2007.


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Series Reversion

• Series reversion is the computation of the coefficients of the inverse function given those of the forward function.


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New Point Target Spectrum Method of Series Reversion (MSR)

• An accurate point target spectrum based on power series is derived

• Solution for the point of stationary phase is given by

• The accuracy is controlled by the degree of the power series• MSR can be used to adapt Monostatic algorithms to process

bistatic data - RDA and NLCS


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Linking - MSR, LBF and DMO

• We established the Link between MSR, LBF and DMO

• Using the MSR, we derived a new form of the point target spectrum using two stationary points.

• Similar to LBF, the phase of MSR can be split into quasi-monostatic and bistatic deformation terms.

• Journal Paper Submitted:Y.L.Neo., F.H. Wong. And I.G. Cumming A Comparison of Point Target Spectra Derived for Bistatic SAR Processing, Transactions for Geoscience and Remote Sensing, submitted for publication,14 Dec 2006.


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Link between MSR and LBFStationary point solutions

Split phase into quasi monostatic and bistatic components

1 2 3 4

MSR LBF

Contribution#

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LBF and DMO• Rocca’s smile operator can be shown to be

LBF’s deformation term if the approximation below is used


Approximation is valid whenbaseline is short whencompared to bistatic Range

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Alternative method to derive the Rocca’s Smile Operator


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Link between MSR, LBF and DMO


MonostaticTerm

Leader - Follower Flight configuration

Quasi-MonostaticTerm

Bistatic Deformation

Term

Expand about Tx and Rx stationary pointsConsider up to Quadratic Phase only

2D Point Target Spectrum

Rocca’s Smile Operator

Baseline is shortCompared to Range

LBF

MSR

DMO

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Bistatic RDA• Developed from the MSR 2D point target spectrum

• Monostatic algorithms like RDA, CSA achieve efficiency by using the azimuth-invariant property

• Bistatic range histories are azimuth-invariant by if baseline is constant

• The MSR is required as range equation is not hyperbolic

• Journal Paper Reviewed and Re-submitted:Y.L.Neo., F.H. Wong. And I.G. Cumming Processing of Azimuth-Invariant Bistatic SAR Data Using the Range Doppler Algorithm, IEEE Transactions for Geoscience and Remote Sensing, re-submitted for publication, 12 Apr 2007.


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Main Processing steps of bistatic RDA

Range FTAzimuth FT

SRC

RCMC

Azimuth IFT

Baseband Signal

Focused Image

Range Compression

Range IFT

Azimuth Compression


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Real Bistatic Data Focused using Bistatic RDA

Copyright © FGAN FHR


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Non-Linear Chirp Scaling• Existing Non-Linear Chirp Scaling

– Based on paper F. H. Wong, and T. S. Yeo, “New Applications of Nonlinear Chirp Scaling in SAR

Data Processing," in IEEE Trans. Geosci. Remote Sensing, May 2001.

– Assumes negligible QRCM (i.e., for SAR with short wavelength)

– Shown to work on Monostatic cases and the Bistatic cases where receiver is stationary

• We have extended NLCS to handle Bistatic non-parallel tracks cases


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Extended NLCS• Able to handle higher resolutions, longer

wavelength cases

• Corrects range curvature, higher order phase terms and SRC

• Develop fast frequency domain matched filter using MSR

• Solve registration to Ground Plane• Journal Paper written:

F.H. Wong., I.G. Cumming and Y.L. Neo, Focusing Bistatic SAR Data using Non-Linear Chirp Scaling Algorithm, IEEE Transactions for Geoscience and Remote Sensing, to be submitted for publication, May 2007.


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Main Processing steps of Extended NLCS

Range compressionLRCMC / Linear phase removal

Azimuth compression

BasebandSignal

FocusedImage

Non-Linear Chirp Scaling

Range CurvatureCorrection

The NLCS scaling function is a

polynomial function of azimuth time

• NLCS applied in the time domain

• SRC and Range Curvature Correction --- range Doppler/2D freq domain

• Azimuth matched filtering --- range Doppler domain

Range Curvature Correction and SRC

Non-Linear Chirp Scaling


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Agenda






• Conclusions

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Concluding Remarks• With our four contributions, a more general and

accurate form of bistatic point target spectrum was derived.

• Using this result, we were able to focus more general bistatic cases using several algorithms that we have developed.

• We plan to work on future projects that make use of the results from this thesis – Interest express from several agencies – DRDC (Ottawa), DSO National Labs (Singapore) and U. of Siegen (Germany).

– Satellite – Airborne (TerraSAR-X and PAMIR)– Satellite/Airborne – stationary receiver (X and C band) using RADARSAT-2 or TerraSAR-X

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Q&AThank You

Documents

1 out of 30 Digital Processing Algorithms for Bistatic Synthetic Aperture Radar Data 4 May 2007 by Y. L. Neo Supervisor : Prof. Ian Cumming Industrial